Neurons live for scores of years, but the ion channels and receptors that are important for signaling in the brain are replaced continuously in hours and days. Therefore, maintaining stable brain function requires understanding how the brain rebuilds itself and renews itself while functioning. Moreover, mechanisms that maintain brain stability must be balanced by processes that allow flexibility for development and learning. This project develops new computational models to understand the homeostatic mechanisms in the brain that maintain stable function despite perturbations of all kinds. A variety of models of varying complexity will be developed and studied. These include biologically plausible self-regulating models that specifically describe mRNA expression and protein synthesis, trafficking and degradation. The goal is not to model all the details of these processes, but to determine how the number of steps involved in regulation, their dynamics, and the way they are coupled affect the qualitative outcome of regulation in neurons. Additionally, models of homeostatic sensors and set points will be built using plausible Ca2+- dependent biochemical reaction schemes. Studies will determine how their reaction rates influence set-points. Finally, homeostatic regulation will be examined in small networks of neurons to determine how homeostatic regulation of synaptic and intrinsic properties can produce compensation to a variety of perturbations. This work will provide insight into the effects of mutations in ion channels on network function, as well as help understand the kinds of compensations that can occur in networks as a response of long-term or short-term pharmacological treatments. Understanding homeostatic regulation in the brain will give insight into nervous system disorders such as epilepsy, chronic pain, long-term pharmacological treatments, changes in reflex pathways subsequent to spinal cord lesions, and a variety of other perturbations to nervous systems.